import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
data=pd.read_csv("C:/Users/Rakesh/Datasets/IPL 2022.csv")
data.head()
| match_id | date | venue | team1 | team2 | stage | toss_winner | toss_decision | first_ings_score | first_ings_wkts | second_ings_score | second_ings_wkts | match_winner | won_by | margin | player_of_the_match | top_scorer | highscore | best_bowling | best_bowling_figure | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | March 26,2022 | Wankhede Stadium, Mumbai | Chennai | Kolkata | Group | Kolkata | Field | 131 | 5 | 133 | 4 | Kolkata | Wickets | 6 | Umesh Yadav | MS Dhoni | 50 | Dwayne Bravo | 3--20 |
| 1 | 2 | March 27,2022 | Brabourne Stadium, Mumbai | Delhi | Mumbai | Group | Delhi | Field | 177 | 5 | 179 | 6 | Delhi | Wickets | 4 | Kuldeep Yadav | Ishan Kishan | 81 | Kuldeep Yadav | 3--18 |
| 2 | 3 | March 27,2022 | Dr DY Patil Sports Academy, Mumbai | Banglore | Punjab | Group | Punjab | Field | 205 | 2 | 208 | 5 | Punjab | Wickets | 5 | Odean Smith | Faf du Plessis | 88 | Mohammed Siraj | 2--59 |
| 3 | 4 | March 28,2022 | Wankhede Stadium, Mumbai | Gujarat | Lucknow | Group | Gujarat | Field | 158 | 6 | 161 | 5 | Gujarat | Wickets | 5 | Mohammed Shami | Deepak Hooda | 55 | Mohammed Shami | 3--25 |
| 4 | 5 | March 29,2022 | Maharashtra Cricket Association Stadium,Pune | Hyderabad | Rajasthan | Group | Hyderabad | Field | 210 | 6 | 149 | 7 | Rajasthan | Runs | 61 | Sanju Samson | Aiden Markram | 57 | Yuzvendra Chahal | 3--22 |
figure=px.bar(data,x=data["match_winner"], title="Number of matches won in Ipl 2022")
figure.show()
data["won_by"]=data["won_by"].map({"Wickets":"Chasing", "Runs":"Defending"})
won_by=data["won_by"].value_counts()
label=won_by.index
counts=won_by.values
colors=['gold','lightgreen']
fig=go.Figure(data=[go.Pie(labels=label,values=counts)])
fig.update_layout(title_text='Number of matches won by defending or chasing')
fig.update_traces(hoverinfo='label+percent', textinfo='value', textfont_size=30, marker=dict(colors=colors,line=dict(color='black', width=3)))
fig.show()
toss=data['toss_decision'].value_counts()
label=toss.index
counts=toss.values
colors=['skyblue','yellow']
fig=go.Figure(data=[go.Pie(labels=label, values=counts)])
fig.update_layout(title_text='Toss Decision')
fig.update_traces(hoverinfo='label+percent',textinfo='value', textfont_size=30, marker=dict(colors=colors,line=dict(color='black',width=3)))
fig.show()
# Top Scorers of IPL 2022
figure=px.bar(data,x=data['top_scorer'],title='Top scorers of ipl 2022')
figure.show()
figure=px.bar(data,x=data['top_scorer'],y=data['highscore'],color=data['highscore'],title='Top scorers in IPL 2022')
figure.show()
figure=px.bar(data,x=data['player_of_the_match'],title='Most player of the match awards')
figure.show()
figure=px.bar(data,x=data['best_bowling'],title='Best Bowlers in Ipl 2022')
figure.show()
#Most of the wickets fall in the first innings or second innings
figure=go.Figure()
figure.add_trace(go.Bar(x=data['venue'],y=data['first_ings_wkts'],name='First Innings Wickets',marker_color='gold'))
figure.add_trace(go.Bar(x=data['venue'],y=data['second_ings_wkts'],name='Second Innings Wickets',marker_color='lightgreen'))
figure.update_layout(barmode='group', xaxis_tickangle=-45)
figure.show()